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Thai, University of Florida Honorary Editor Ding-Zhu Du, University of Texas at Dallas Advisory Editors Roman V. Springer Optimization and Its Applications Volume 158
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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization Springer Optimization and Its Applications 158
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1 Introduction: Overview of Unconstrained Optimizationġ.3 Optimality Conditions for Unconstrained Optimizationġ.4 Overview of Unconstrained Optimization Methodsġ.4.5 Quasi-Newton Methods with Diagonal Updating of the Hessianġ.4.6 Limited-Memory Quasi-Newton MethodsĢ.2 Fundamental Property of the Line Search Method with Conjugate DirectionsĢ.3 The Linear Conjugate Gradient AlgorithmĢ.4 Convergence Rate of the Linear Conjugate Gradient AlgorithmĢ.5 Comparison of the Convergence Rate of the Linear Conjugate Gradient and of the Steepest DescentĢ.6 Preconditioning of the Linear Conjugate Gradient Algorithmsģ General Convergence Results for Nonlinear Conjugate Gradient Methodsģ.2 The Concept of Nonlinear Conjugate Gradientģ.3 General Convergence Results for Nonlinear Conjugate Gradient Methodsģ.3.1 Convergence Under the Strong Wolfe Line Searchģ.3.2 Convergence Under the Standard Wolfe Line SearchĤ.1 Conjugate Gradient Methods with \left\| Ī.4 Elements of Convexity-Convex Sets and Convex FunctionsĪppendix B UOP: A Collection of 80 Unconstrained Optimization Test Problems
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